GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption

Machine Learning (ML) has emerged as one of data science's most transformative and influential domains. However, the widespread adoption of ML introduces privacy-related concerns owing to the increasing number of malicious attacks targeting ML models. To address these concerns, Privacy-Preservi...

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Veröffentlicht in:	arXiv.org 2024-01
Hauptverfasser:	Frimpong, Eugene, Nguyen, Khoa, Budzys, Mindaugas, Khan, Tanveer, Michalas, Antonis
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification Cost analysis Cryptography Data science Encryption Heart diseases Machine learning Privacy
Online-Zugang:	Volltext
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